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This study explores the crucial interplay between aggregators and buildingoccupants in activating flexibility through Demand Response (DR) programs, witha keen focus on achieving robust decarbonization and fortifying the resilienceof the energy system amidst the uncertainties presented by Renewable EnergySources (RES). Firstly, it introduces a methodology of optimizing aggregatedflexibility provision strategies in environments with limited data, utilizingDiscrete Fourier Transformation (DFT) and clustering techniques to identifybuilding occupant's activity patterns. Secondly, the study assesses thedisaggregated flexibility provision of Heating Ventilation and Air Conditioning(HVAC) systems during DR events, employing machine learning and optimizationtechniques for precise, device-level analysis. The first approach offers anon-intrusive pathway for aggregators to provide flexibility services inenvironments of a single smart meter for the whole building's consumption,while the second approach carefully considers building occupants' thermalcomfort profiles, while maximizing flexibility in case of existence ofdedicated smart meters to the HVAC systems. Through the application ofdata-driven techniques and encompassing case studies from both industrial andresidential buildings, this paper not only unveils pivotal opportunities foraggregators in the balancing and emerging flexibility markets but alsosuccessfully develops end-to-end practical tools for aggregators. Furthermore,the efficacy of this tool is validated through detailed case studies,substantiating its operational capability and contributing to the evolution ofa resilient and efficient energy system.

Empowering Aggregators with Practical Data-Driven Tools: Harnessing Aggregated and Disaggregated Flexibility for Demand Response